Data, Information, and Knowledge Study Guide
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An information systems study guide from the University of Minnesota.
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Data, Information, and Knowledge - Study Guide
The amount of data on corporate hard drives doubles every six months. In many organizations, available data is not exploited to advantage.
Data is oftentimes considered a defensible source of competitive advantage; however, advantages based on capabilities and data that others can acquire will be short-lived.
Data includes raw facts that must be turned into information in order to be useful and valuable. Databases are created, maintained, and manipulated using programs called database management systems (DBMS), sometimes referred to as database software.
All data fields in the same database have unique names, several data fields make up a data record, multiple data records make up a table or data file, and one or more tables or data files make up a database. Relational databases are the most common database format.
Name and define the terms that are supplanting discussions of decision support systems in the modern IS lexicon.
Is data a source of competitive advantage? Describe situations in which data might be a source for sustainable competitive advantage. When might data not yield sustainable advantage?
Are advantages based on analytics and modeling potentially sustainable? Why or why not?
What role do technology and timing play in realizing advantages from the data asset?
Define the following terms: table, record, field. Provide another name for each term along with your definition.
Research to find additional examples of organizations that made bad decisions based on bad data. Report your examples to your class. What was the end result of the examples you’re citing (e.g., loss, damage, or other outcome)? What could managers have done to prevent problems in the cases that you cited? What role did technology play in the examples that you cite? What role did people or procedural issues play?
Why is an understanding of database terms and technologies important, even for nontechnical managers and staff? Consider factors associated with both system use and system development. What other skills, beyond technology, may be important when engaged in data-driven decision making?
11.1 Introduction and 11.2 Data, Information, and Knowledge by University of Minnesota are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.